For the purpose of analysing images, methods are known from the prior art which provide pixel-by-pixel evaluation. For example, a Markov model or CRF (Conditional Random Field) model is frequently used. These models calculate the connections of adjacent pixels (nodes). The information which can be extracted locally from an image section is however restricted in the standard formulation of a CRF model. Edge potentials in pairs are too weak in order to model interactions at a large spatial distance. Ideally, a complete, tightly filled layer with unobservable nodes for all layers and scales would be added to the CRF standard model. However, it would be take a lot of time or be very costly to calculate inferences in such a model, however, due to the required level of complexity of the calculation.